Natural Language Processing is a rapidly growing field that provides core algorithms and methods for work in Artificial Intelligence and in Computer Science in general. The M.S. in Natural Language Processing curriculum is designed to provide the breadth and depth of knowledge needed for a successful career in Natural Language Processing. It emphasizes practical proficiency in applying the relevant skills through courses focusing on core algorithms in Natural Language Processing, machine learning, and data science and analytics. Electives offer students the opportunity to acquire more specialized knowledge in particular NLP application areas. The M.S. is a one calendar-year program, beginning in the fall quarter. The program includes a three quarter capstone project where students will get real-world experience working in small groups working on an industry-relevant challenging NLP problem.
- NLP 201, Natural Language Processing I
- NLP 220, Data Collection, Wrangling and Crowdsourcing
- NLP 243, Machine Learning for NLP
- NLP 280, Seminar
- NLP 202, Natural Language Processing II
- NLP 271A, Capstone I (Project Exploration)
- NLP 280, Seminar
- NLP 270, Linguistic Models of Syntax & Semantics for Computer Scientists
- NLP 203, Natural Language Processing III
- NLP 271B, Capstone II (Project Definition)
- NLP 244, Advanced Machine Learning for Natural Language Processing
- NLP 271C, Capstone III (Project Implementation)
Course offerings may vary from year to year.
The 2020-2021 NLP degree requirements can be found in the UCSC Catalog.
The minimum unit requirement for the M.S. Degree in Natural Language Processing is 50 units. Unit requirement breakdown:
- 25 units - Core Courses
- 10 units - Elective Track
- 13 units - Capstone Project
- 2 units - NLP Seminar
All students are required to enroll and pass (letter grade "B-" or better) the following six courses:
- NLP 201, Natural Language Processing I (5 units)
- NLP 202, Natural Language Processing II (5 units)
- NLP 203, Natural Language Processing III (5 units)
- NLP 220, Data Collection, Wrangling & Crowdsourcing (5 units)
- NLP 243, Machine Learning for Natural Language Processing (5 units)
- NLP 280, Seminar in NLP (2 units)
All students are required to enroll and pass (letter grade "B-" or better) in a minimum of two of the following elective courses:
- NLP 244, Advanced Machine Learning for Natural Language Processing (5 units)
- NLP 245, Conversational Agents (5 units)
- NLP 255, Topics in Applied Natural Language Processing (5 units)
- NLP 267, Machine Translation (5 units)
- NLP 270, Linguistic Models of Syntax & Semantics for Computer Scientists (5 units)
- CSE 245 / LING 245 / CMPM 245, Computational Models of Discourse and Dialogue (5 units)
- CSE 272, Information Retrieval (5 units)
- CSE 290C, Advanced Topics in Machine Learning (5 units)
- CSE 290K, Advanced Topics in Natural Language Processing (5 units)
Elective offerings may vary from year to year.
Capstone Project Courses
All students are required to enroll and pass (letter grade "B-" or better) the Capstone Project series:
- NLP 271A, Capstone Project I (3 units, Winter quarter)
- NLP 271B, Capstone Project II (5 units, Spring quarter)
- NLP 271C, Capstone Project III (5 units, Summer quarter)
The capstone requirement for the M.S. Degree is fulfilled through an application team project. Students are expected to work on their capstone requirement starting in the winter quarter, with refinements and final pitches in the spring quarter, and final execution of the project during the summer. Teams will be made of four to five students, who will work collaboratively on the project.
The teamwork will be spread over a 3 unit class in winter (NLP 271A), a 5 unit class in spring (NLP 271B) and a 5 unit class in summer (NLP 271C) to constitute the complete 13 unit capstone experience. Teams will need to do oral presentations of multiple possible project proposals during the Winter quarter. Then after feedback and refinement, these will result in 5 to 10 page written proposals that will be orally presented during the Spring Quarter. The proposal will detail the team membership, pitch the topic of the project, and detail the sources of data to be used, which will need to be approved by the capstone coordinator (typically, the Executive Director of the program). Each team will be assigned a faculty mentor from among the program faculty or an industrial mentor, who will meet with the team at least once a week to provide guidance and evaluate progress. The evaluation will assess the team’s final product, their group process (e.g., the ability to meet deadlines, generate a range of ideas, listen respectfully to disparate perspectives, distribute work fairly, resolve differences, and communicate effectively), and their individual contributions to the final product. Product evaluation will be based on a final written assessment by the faculty or industrial mentor based on the a final report and presentation to be presented at the annual Natural Language Processing Fair (see below). Group process evaluation will be based on written team evaluations (in which each member evaluates the dynamics of the group as a whole) and written evaluations by the mentor produced on weeks 3, 7 and 10 of the summer quarter. Individual evaluations will be based on peer reviews (each team member evaluates the contributions of his/her teammates), self-evaluations (each team member documents and evaluates his own contributions to the team), and an individual 3 page report written by each team member.
All students will be required to either present a poster or oral presentation at the Natural Language Processing Fair, which will be an integral part of the capstone evaluation. The Natural Language Processing Fair will be an annual one-day event taking place at the end of each Summer term to which program faculty, students and members of the Industry Advisory Board will be encouraged to attend. The fair will also serve as a general outreach to NLP scientists in local industry and government.
International students graduating from the NLP M.S. program who are in valid F-1 status are eligible for OPT. Graduates of the NLP M.S. program are also eligible for the OPT STEM Extension. The CIP code for NLP is 11.0102, which is the same as Artificial Intelligence, as listed on the DHS STEM Designated Degree Program List. OPT-related questions should be directed to the International Student and Scholar Services (ISSS) Office at firstname.lastname@example.org.